In today's asymmetrical military operations warfighters may daily face new threats which may place them in harm's way. Identification and further classification of these threats may present a valuable tool to enable a warfighter to more successfully resolve a threatening situation.
Current solutions may be based on a priori fixed policies. Unfortunately, such solutions may be static in nature and unable to handle previously unknown situations. In a wartime environment, there may be situations where the dynamic nature of the battleground lends itself to a constantly evolving situation. A static solution may lag what is currently present and required for the warfighter to evaluate. Currently, there are no behavior based friend/foe/neutral determiners which may provide the needed time critical flexibility.
Previous approaches attempting to solve an identification problem have been developed for RF Electronic warfare (EW). Some approaches may include electronic situation awareness (ESA), Electronic Protection (EP) and Electronic attack (EA). Most of these systems may operate from a standoff distance and are statically trained for certain types of threats (i.e. RF signals) prior to deployment. These systems may not be able to automatically adapt to new and previously unknown threats that may arise in the monitored areas. When these static systems encounter a new threat, data for the new threat is collected and the systems may require off-battlefield maintenance for retraining. Similarly, the collected threat data may be sent to an analyst to determine what techniques may be needed to counter the threat. This process may be considerably time consuming, taking from a day to several months for analysis and re-deployment. Within this retraining period, adversaries may have introduced new threats against which the warfighter may be required to overcome. Furthermore, EA systems historically used for countering threats may generally create a “dead zone” of high energy noise which may inhibit desirable communication.
Identification and classification of a perceived entity based on received RF communications may be beneficial to a wide variety of applications such as Electronic Intelligence (ELINT), Communications Intelligence (COMINT) and Electronic warfare (EW).
Therefore, a novel approach may be employed to receive RF energy from a perceived entity and accurately identify and classify the entity based on a modeling analysis of the RF energy.